{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通用教程简介(Introduction To ggplot2)  \n",
    "以前，我们看到了使用ggplot2软件包制作图表的简短教程。它很快涉及制作ggplot的各个方面。现在，这是一个完整而完整的教程。现在讨论如何构造和自定义几乎所有ggplot。它涉及的原则，步骤和微妙之处，使图像的情节有效和更具视觉吸引力。因此，出于实用目的，我希望本教程可以作为书签参考，对您日常的绘图工作很有用。\n",
    "这是ggplot2的三部分通用教程的第1部分，ggplot2是R中的美观（非常流行）的图形框架。该教程主要针对具有R编程语言的一些基本知识并希望制作复杂且美观的图表的用户与R ggplot2。\n",
    "+ ggplot2简介(Introduction to ggplot2)\n",
    "+ 自定义外观(Customizing the Look and Feel)\n",
    "+ 前50个ggplot2可视化效果(top 50 ggplot2 Visualizations)\n",
    "\n",
    "ggplot2简介涵盖了有关构建简单ggplot以及修改组件和外观的基本知识；自定义外观是关于图像的自定义，如使用多图，自定义布局操作图例、注释；前50个ggplot2可视化效果应用在第1部分和第2部分中学到的知识来构造其他类型的ggplot，例如条形图，箱形图等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 ggplot2入门笔记2—通用教程ggplot2简介\n",
    "本章节简介涵盖了有关构建简单ggplot以及修改组件和外观的基本知识，该章节主要内容有：\n",
    "1. 了解ggplot语法(Understanding the ggplot Syntax)\n",
    "2. 如何制作一个简单的散点图(How to Make a Simple Scatterplot)\n",
    "3. 如何调整XY轴范围(How to Adjust the X and Y Axis Limits)\n",
    "4. 如何更改标题和轴标签(How to Change the Title and Axis Labels)\n",
    "5. 如何更改点的颜色和大小(How to Change the Color and Size of Points)\n",
    "6. 如何更改X轴文本和刻度的位置(How to Change the X Axis Texts and Ticks Location)\n",
    "\n",
    "**参考文档**\n",
    "> http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 了解ggplot语法(Understanding the ggplot Syntax)\n",
    "如果您是初学者或主要使用基本图形，则构造ggplots的语法可能会令人困惑。主要区别在于，与基本图形不同，ggplot适用于数据表而不是单个矢量。绘图所需的所有数据通常都包含在提供给ggplot（）本身的数据框中，或者可以提供给各个geom。第二个值得注意的功能是，您可以通过向使用该ggplot()功能创建的现有图上添加更多层（和主题）来继续增强图。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们根据midwest数据集初始化一个基本的ggplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 3.6.1\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 6 × 28</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>PID</th><th scope=col>county</th><th scope=col>state</th><th scope=col>area</th><th scope=col>poptotal</th><th scope=col>popdensity</th><th scope=col>popwhite</th><th scope=col>popblack</th><th scope=col>popamerindian</th><th scope=col>popasian</th><th scope=col>...</th><th scope=col>percollege</th><th scope=col>percprof</th><th scope=col>poppovertyknown</th><th scope=col>percpovertyknown</th><th scope=col>percbelowpoverty</th><th scope=col>percchildbelowpovert</th><th scope=col>percadultpoverty</th><th scope=col>percelderlypoverty</th><th scope=col>inmetro</th><th scope=col>category</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>...</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>561</td><td>ADAMS    </td><td>IL</td><td>0.052</td><td>66090</td><td>1270.9615</td><td>63917</td><td>1702</td><td>98</td><td>249</td><td>...</td><td>19.63139</td><td>4.355859</td><td>63628</td><td>96.27478</td><td>13.151443</td><td>18.01172</td><td>11.009776</td><td>12.443812</td><td>0</td><td>AAR</td></tr>\n",
       "\t<tr><td>562</td><td>ALEXANDER</td><td>IL</td><td>0.014</td><td>10626</td><td> 759.0000</td><td> 7054</td><td>3496</td><td>19</td><td> 48</td><td>...</td><td>11.24331</td><td>2.870315</td><td>10529</td><td>99.08714</td><td>32.244278</td><td>45.82651</td><td>27.385647</td><td>25.228976</td><td>0</td><td>LHR</td></tr>\n",
       "\t<tr><td>563</td><td>BOND     </td><td>IL</td><td>0.022</td><td>14991</td><td> 681.4091</td><td>14477</td><td> 429</td><td>35</td><td> 16</td><td>...</td><td>17.03382</td><td>4.488572</td><td>14235</td><td>94.95697</td><td>12.068844</td><td>14.03606</td><td>10.852090</td><td>12.697410</td><td>0</td><td>AAR</td></tr>\n",
       "\t<tr><td>564</td><td>BOONE    </td><td>IL</td><td>0.017</td><td>30806</td><td>1812.1176</td><td>29344</td><td> 127</td><td>46</td><td>150</td><td>...</td><td>17.27895</td><td>4.197800</td><td>30337</td><td>98.47757</td><td> 7.209019</td><td>11.17954</td><td> 5.536013</td><td> 6.217047</td><td>1</td><td>ALU</td></tr>\n",
       "\t<tr><td>565</td><td>BROWN    </td><td>IL</td><td>0.018</td><td> 5836</td><td> 324.2222</td><td> 5264</td><td> 547</td><td>14</td><td>  5</td><td>...</td><td>14.47600</td><td>3.367680</td><td> 4815</td><td>82.50514</td><td>13.520249</td><td>13.02289</td><td>11.143211</td><td>19.200000</td><td>0</td><td>AAR</td></tr>\n",
       "\t<tr><td>566</td><td>BUREAU   </td><td>IL</td><td>0.050</td><td>35688</td><td> 713.7600</td><td>35157</td><td>  50</td><td>65</td><td>195</td><td>...</td><td>18.90462</td><td>3.275891</td><td>35107</td><td>98.37200</td><td>10.399635</td><td>14.15882</td><td> 8.179287</td><td>11.008586</td><td>0</td><td>AAR</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 28\n",
       "\\begin{tabular}{r|llllllllllllllllllllllllllll}\n",
       " PID & county & state & area & poptotal & popdensity & popwhite & popblack & popamerindian & popasian & popother & percwhite & percblack & percamerindan & percasian & percother & popadults & perchsd & percollege & percprof & poppovertyknown & percpovertyknown & percbelowpoverty & percchildbelowpovert & percadultpoverty & percelderlypoverty & inmetro & category\\\\\n",
       " <int> & <chr> & <chr> & <dbl> & <int> & <dbl> & <int> & <int> & <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t 561 & ADAMS     & IL & 0.052 & 66090 & 1270.9615 & 63917 & 1702 & 98 & 249 &  124 & 96.71206 &  2.5752761 & 0.1482826 & 0.37675897 & 0.18762294 & 43298 & 75.10740 & 19.63139 & 4.355859 & 63628 & 96.27478 & 13.151443 & 18.01172 & 11.009776 & 12.443812 & 0 & AAR\\\\\n",
       "\t 562 & ALEXANDER & IL & 0.014 & 10626 &  759.0000 &  7054 & 3496 & 19 &  48 &    9 & 66.38434 & 32.9004329 & 0.1788067 & 0.45172219 & 0.08469791 &  6724 & 59.72635 & 11.24331 & 2.870315 & 10529 & 99.08714 & 32.244278 & 45.82651 & 27.385647 & 25.228976 & 0 & LHR\\\\\n",
       "\t 563 & BOND      & IL & 0.022 & 14991 &  681.4091 & 14477 &  429 & 35 &  16 &   34 & 96.57128 &  2.8617170 & 0.2334734 & 0.10673071 & 0.22680275 &  9669 & 69.33499 & 17.03382 & 4.488572 & 14235 & 94.95697 & 12.068844 & 14.03606 & 10.852090 & 12.697410 & 0 & AAR\\\\\n",
       "\t 564 & BOONE     & IL & 0.017 & 30806 & 1812.1176 & 29344 &  127 & 46 & 150 & 1139 & 95.25417 &  0.4122574 & 0.1493216 & 0.48691813 & 3.69733169 & 19272 & 75.47219 & 17.27895 & 4.197800 & 30337 & 98.47757 &  7.209019 & 11.17954 &  5.536013 &  6.217047 & 1 & ALU\\\\\n",
       "\t 565 & BROWN     & IL & 0.018 &  5836 &  324.2222 &  5264 &  547 & 14 &   5 &    6 & 90.19877 &  9.3728581 & 0.2398903 & 0.08567512 & 0.10281014 &  3979 & 68.86152 & 14.47600 & 3.367680 &  4815 & 82.50514 & 13.520249 & 13.02289 & 11.143211 & 19.200000 & 0 & AAR\\\\\n",
       "\t 566 & BUREAU    & IL & 0.050 & 35688 &  713.7600 & 35157 &   50 & 65 & 195 &  221 & 98.51210 &  0.1401031 & 0.1821340 & 0.54640215 & 0.61925577 & 23444 & 76.62941 & 18.90462 & 3.275891 & 35107 & 98.37200 & 10.399635 & 14.15882 &  8.179287 & 11.008586 & 0 & AAR\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 28\n",
       "\n",
       "| PID &lt;int&gt; | county &lt;chr&gt; | state &lt;chr&gt; | area &lt;dbl&gt; | poptotal &lt;int&gt; | popdensity &lt;dbl&gt; | popwhite &lt;int&gt; | popblack &lt;int&gt; | popamerindian &lt;int&gt; | popasian &lt;int&gt; | ... ... | percollege &lt;dbl&gt; | percprof &lt;dbl&gt; | poppovertyknown &lt;int&gt; | percpovertyknown &lt;dbl&gt; | percbelowpoverty &lt;dbl&gt; | percchildbelowpovert &lt;dbl&gt; | percadultpoverty &lt;dbl&gt; | percelderlypoverty &lt;dbl&gt; | inmetro &lt;int&gt; | category &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 561 | ADAMS     | IL | 0.052 | 66090 | 1270.9615 | 63917 | 1702 | 98 | 249 | ... | 19.63139 | 4.355859 | 63628 | 96.27478 | 13.151443 | 18.01172 | 11.009776 | 12.443812 | 0 | AAR |\n",
       "| 562 | ALEXANDER | IL | 0.014 | 10626 |  759.0000 |  7054 | 3496 | 19 |  48 | ... | 11.24331 | 2.870315 | 10529 | 99.08714 | 32.244278 | 45.82651 | 27.385647 | 25.228976 | 0 | LHR |\n",
       "| 563 | BOND      | IL | 0.022 | 14991 |  681.4091 | 14477 |  429 | 35 |  16 | ... | 17.03382 | 4.488572 | 14235 | 94.95697 | 12.068844 | 14.03606 | 10.852090 | 12.697410 | 0 | AAR |\n",
       "| 564 | BOONE     | IL | 0.017 | 30806 | 1812.1176 | 29344 |  127 | 46 | 150 | ... | 17.27895 | 4.197800 | 30337 | 98.47757 |  7.209019 | 11.17954 |  5.536013 |  6.217047 | 1 | ALU |\n",
       "| 565 | BROWN     | IL | 0.018 |  5836 |  324.2222 |  5264 |  547 | 14 |   5 | ... | 14.47600 | 3.367680 |  4815 | 82.50514 | 13.520249 | 13.02289 | 11.143211 | 19.200000 | 0 | AAR |\n",
       "| 566 | BUREAU    | IL | 0.050 | 35688 |  713.7600 | 35157 |   50 | 65 | 195 | ... | 18.90462 | 3.275891 | 35107 | 98.37200 | 10.399635 | 14.15882 |  8.179287 | 11.008586 | 0 | AAR |\n",
       "\n"
      ],
      "text/plain": [
       "  PID county    state area  poptotal popdensity popwhite popblack popamerindian\n",
       "1 561 ADAMS     IL    0.052 66090    1270.9615  63917    1702     98           \n",
       "2 562 ALEXANDER IL    0.014 10626     759.0000   7054    3496     19           \n",
       "3 563 BOND      IL    0.022 14991     681.4091  14477     429     35           \n",
       "4 564 BOONE     IL    0.017 30806    1812.1176  29344     127     46           \n",
       "5 565 BROWN     IL    0.018  5836     324.2222   5264     547     14           \n",
       "6 566 BUREAU    IL    0.050 35688     713.7600  35157      50     65           \n",
       "  popasian ... percollege percprof poppovertyknown percpovertyknown\n",
       "1 249      ... 19.63139   4.355859 63628           96.27478        \n",
       "2  48      ... 11.24331   2.870315 10529           99.08714        \n",
       "3  16      ... 17.03382   4.488572 14235           94.95697        \n",
       "4 150      ... 17.27895   4.197800 30337           98.47757        \n",
       "5   5      ... 14.47600   3.367680  4815           82.50514        \n",
       "6 195      ... 18.90462   3.275891 35107           98.37200        \n",
       "  percbelowpoverty percchildbelowpovert percadultpoverty percelderlypoverty\n",
       "1 13.151443        18.01172             11.009776        12.443812         \n",
       "2 32.244278        45.82651             27.385647        25.228976         \n",
       "3 12.068844        14.03606             10.852090        12.697410         \n",
       "4  7.209019        11.17954              5.536013         6.217047         \n",
       "5 13.520249        13.02289             11.143211        19.200000         \n",
       "6 10.399635        14.15882              8.179287        11.008586         \n",
       "  inmetro category\n",
       "1 0       AAR     \n",
       "2 0       LHR     \n",
       "3 0       AAR     \n",
       "4 1       ALU     \n",
       "5 0       AAR     \n",
       "6 0       AAR     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CE5d4cTknN3OCE5d4cTknN3OCE5d4cTknN3OCE5d4f7F9hIqxEsaTDVAAAAAElF\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup\n",
    "# #关闭科学记数法，如1e+06\n",
    "# turn off scientific notation like 1e+06\n",
    "options(scipen=999)  \n",
    "library(ggplot2)\n",
    "# load the data 载入数据\n",
    "data(\"midwest\", package = \"ggplot2\")\n",
    "# 显示数据\n",
    "head(midwest)\n",
    "# Init Ggplot 初始化图像\n",
    "# area and poptotal are columns in 'midwest'\n",
    "ggplot(midwest, aes(x=area, y=poptotal))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面绘制了一个空白ggplot。即使指定了x和y，也没有点或线。这是因为ggplot并不假定您要绘制散点图或折线图。我只告诉ggplotT使用什么数据集，哪些列应该用于X和Y轴。我没有明确要求它画出任何点。还要注意，该aes()功能用于指定X和Y轴。这是因为，必须在aes()函数中指定属于源数据帧的任何信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 如何制作一个简单的散点图(How to Make a Simple Scatterplot)\n",
    "让我们通过使用称为的geom层添加散点图，在空白ggplot基础制作一个散点图geom_point。\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k98K59fo5Aq0XjiW/n8GoVUicYT38rn\n1yikSjSe+FY+v0YhVaLxxLfyOdkgVaLxxLfyt/450svfiLQPnvhWPq9skCrReOJb+duJ9MJr\n7XbFE9/K3/xkwy9Of++DJ76Vv51If3V/XmVX7c+6MMwhZrjn+fby3+Wp3QvHSLvgiW/lb/nU\nrvKrGw6/1F5+ePzmlweRGsET38rnB7JSJRpPfCsfkaRKNJ74Vv7Wr2zgN2T3whPfyueVDVIl\nGk98K387kXgXoX3xxLfyt/81Cl7ZsA+e+FY+IkmVaDzxrXye2kmVaDzxrXxONkiVaDzxrXxO\nf0uVaDzxrXx+ICtVovHEt/IRSapE44lv5W/91I73bNgLT3wrn5MNUiUaT3wrfzuReM+GffHE\nt/I3/4Es79mwE574Vv52IlV/z4bDL7WXHx6/+eWZFIn3bNgXT3wrf8undvyq+Z544lv5iCRV\novHEt/L5gaxUicYT38pHJKkSjSe+lY9IUiUaT3wrH5GkSjSe+FY+IkmVaDzxrXxEkirReOJb\n+YgkVaLxxLfyEUmqROOJb+UjklSJxhPfykckqRKNJ76Vj0hSJRpPfCsfkaRKNJ74Vj4iSZVo\nPPGtfESSKtF44lv5iCRVovHEt/IRSapE44lv5SOSVInGE9/KRySpEo0nvpWPSFIlGk98Kx+R\npEo0nvhWPiJJlWg88a18RJIq0XjiW/mIJFWi8cS38hFJqkTjiW/lI5JUicYT38pHJKkSjSe+\nlY9IUiUaT3wrH5GkihXfdd2m/EcnfPWbXx5EqoN/+1MDG/IfnvDVb355EKkK/vpHOzbjPz7h\nq9/88iBSFTwihfMRSaoY8YgUzkckqeLEc4yUzUckqWLFc9Yumo9IUiUaT3wrH5GkSjSe+FY+\nIkmVaDzxrXxEkirReOJb+YgkVaLxxLfyEUmqROOJb+UjklSJxhPfyt9VpNPvueczIjXAD4/f\n/PKsEOl0/bD2MyK1wA+P3/zyIFIjeOJb+bsfIyFSKD88fvPLs4tIXy6zCM8w6bNIpNOZPVIm\nPzx+88uDSI3giW/l7yzSST8gUhI/PH7zy7NKpFPPJkRK4ofHb3551oh06u+WECmJHx6/+eVZ\nIdLpdH2pAq9sCOSHx29+edbskR6bwy+1lx8ev/nlQaRG8MS38hFJqkTjiW/lI5JUicYT38pH\nJKkSjSe+lY9IUiUaT3wrH5GkSjSe+FY+IkmVaDzxrXxEkirReOJb+YgkVaLxxLfyEUmqROOJ\nb+UjklSJxhPfykckqRKNJ76Vj0hSJRpPfCsfkaRKNJ74Vj4iSZVoPPGtfESSKtF44lv5iCRV\novHEt/IRSapE44lv5SOSVInGE9/KRySpEo0nvpWPSFIlGk98Kx+RpEo0nvhWPiJJlWg88a18\nRJIq0XjiW/mIJFWi8cS38hFJqkTjiW/lI5JUicYT38pHJKkSjSe+lY9IUiUaT3wrH5GkSjSe\n+FY+IkmVaDzxrXxEkirReOJb+YgkVaLxxLfyEUmqROOJb+UjklSJxhPfykckqRKNJ76Vj0hS\nJRpPfCsfkaRKNJ74Vj4iSZVoPPGtfESSKtF44lv5iCRVovHEt/IRSapE44lv5SOSVInGE9/K\nRySpEo0nvpWPSFIlGk98Kx+RpEo0nvhWPiJJlWg88a18RJIq0XjiW/mIJFWi8cS38hFJqkTj\niW/lI5JUicYT38pHJKkSjSe+lY9IUiUaT3wrH5GkSjSe+FY+IkmVaDzxrXxEkirReOJb+Ygk\nVaLxxLfyEUmqROOJb+UjklSJxhPfykckqRKNJ76Vj0hSJRpPfCs/SCSGOcSwR7LiiW/lB+2R\nDr/UXn54/OaXB5EawRPfykckqRKNJ76Vj0hSJRpPfCsfkaRKNJ74Vj4iSZVoPPGtfESSKtF4\n4lv5iCRVovHEt/IRSarUgHRdtyX+xrS+pTw3H5GkSgVG102alBDfh0/nI5JUeRzRddMmBcQ3\n4tP5iCRVHkcg0lH5iCRVHkcg0lH5iCRVKjA4RjooH5GkSg0IZ+2OyUckqRKNJ76Vj0hSJRpP\nfCsfkaRKNJ74Vj4iSZVoPPGtfESSKtF44lv5iCRVovHEt/IRSapE44lv5SOSVInGE9/KRySp\nEo0nvpWPSFIlGk98Kx+RpEo0nvhWPiJJlWg88a18RJIq0XjiW/mIJFWi8cS38hFJqkTjiW/l\nI5JUicYT38pHJKkSjSe+lY9IUiUaT3wrH5GkSjSe+FY+IkmVaDzxrXxEkirReOJb+YgkVaLx\nxLfyEUmqROOJb+UjklSJxhPfykckqRKNJ76Vj0hSJRpPfCsfkaRKNJ74Vj4iSZVoPPGtfESS\nKtF44lv5iCRVovHEt/IRSapE44lv5SOSVInGE9/KRySpEo0nvpWPSFIlGk98Kx+RpEo0nvhW\nPiJJlWg88a18RJIq0XjiW/mIJFWi8cS38hFJqkTjH+V3XbclfnbC+YgkVaLxD/K7bsaktuPb\n+YgkVaLxj/G7bs6kpuP7+YgkVaLxiGTlI5JUicYjkpWPSFIlGs8xkpWPSFIlGs9ZOysfkaRK\nNJ74Vj4iSZVoPPGtfESSKtF44lv5iCRVovHEt/J3Fun05+PvWfMZkRrgh8dvfnlWiXT14/ph\n6WdEaoEfHr/55Vkj0umMSLH88PjNL8+qPRIi5fLD4ze/PLuI9OUyC/AMkz/skax44lv5GXuk\nXUSae43MozMZv9IDt76lPDcfkd5n9lWbj85U/FoP3PqW8tx8RLrO/O8RPDoT8as9cOtbynPz\nEek6iOTFp/N5ZcN1EMmLT+fzWrv34RjJik/nI9LHcNbOiU/nI5JUicYT38pHJKkSjSe+lY9I\nUiUaT3wrH5GkSjSe+FY+IkmVaDzxrXxEkirReOJb+YgkVR67+5O/MVx4/OaXB5Gu8+xvVRoe\nv/nlQaQ/8/Rvnh0ev/nlQaQ/g0jH5iOSVHnkzoh0bD4iSZWH7s0x0qH5iCRVHrs7Z+2OzEck\nqRKNJ76Vj0hSJRpPfCsfkaRKNJ74Vj4iSZXV91j1K3ntxW8Jn85HJKmy9g7rfkm8ufhN4dP5\niCRVbl89kmbl25YcfUt5bj4iSZWb146lQST49fBHEalgDSLBr4c/sEgcI8Gvhz+ySJy1g18N\nfxSRHn8fx6NvKc/NRySpcvvqR9/H8ehbynPzEUmqROOJb+UjklSJxhPfykckqVKd2Hs2mBd/\nT3w6H5GkSm1g//xEXPxd8el8RJIqlXmDM+Zp8ffFp/MRSapU5iHSgfiIJFUq8xDpQHxEkiq1\ngRwjHYePSFKlOpGzdofhI5JUicYT38pHJKkSjSe+lY9IUiUaT3wrH5GkSjSe+FY+IkmVaDzx\nrXxEkirReOJb+YgkVaLxxLfyEUmqROOJb+UjklSJxhPfykckqRKNJ76Vj0hSJRpPfCsfkaRK\nNJ74Vj4iSZVoPPGtfESSKvVQpbfuCopvwKfzEUmqVCMV30wyJ74Dn85HJKlSC1R+d/2Y+BZ8\nOh+RpEotECIdj49IUqUWCJGOx0ckqVKNxDHS4fiIJFXqoThrdzQ+IkmV6kTe/OQwfESSKnfe\nb/LvvfB2XMfhI5JUue9uk3+BjDeIPBAfkaTKXfea/pPMbYl08L+ThkiItHBu84/+lzsRKVek\nlo6RbqSsga8w4fwgkVqdty108qriv/efq0jGBMzSOegeaeHRx8NPrWaHPZKTH7RHamOp79wa\nH9+QZ4djJCcfkaTKgtvcuz3aReKsXeP4Y4l0tw9+kRrHp/MRSarM3+R+H8zHSM3j0/mIJFXm\nb/LAjmXV3e55jNa3lOfmI5JUWXCbB3YsK+Lf9SitbynPzUckqbLkRvc/QVse/779XutbynPz\nEUmqtIJHpDw+IkmVVvCIlMdHJKnSDJ5jpDg+IkmVdvCctUvjI5JUicYT38pHJKkSjSe+lY9I\nUsWEr/SSh9a3lNH0ez/rV3cxAJEexNd68VDai1YHvZ/0q7scgEiP4au9nDXs1yiGvZ/zq7sC\ngEiP4fcRqb1f7EOkAQCRHsMj0jb84SDSk4u0zzFSeyJxjDQAINKj+F3O2jV3jMRZuwEAkebm\nusGY4zd31u65+IgkVbbBvu8LQuPvhE/nI5JU2YT6cXRS5andjdu2vqU8Nx+RpMom1FmR1hy9\n3Lpt61vKc/MRSapUJ162+jmR1pxPu3nb1reU5+YjklSpDfyz1c8cIyHSU/ARSapU5r1v9rfP\n2iHSU/ARSarceb+pTbuTmcYj0lPwEUmq3He3yW2765v0oEi9463StL6lPDcfkaTKXfe6sXH3\nRJrZby18mOnbLt2r3Tttrn4zfESSKnfda27jXmJAHY+i31o8nY9IUuWue83tkersSyrt1x6Y\nNle/GT4iSZX77jb3zG7+6KZ41zLrZghE8vERSarceb8le4nyhr7iedqMJ4hk5h9bpP6mt+mv\nrk14tOTPoX9cOPtg92edn/ANHZE2FGmw8W3Ln/CotPXftXsp3KGqWuEbOiJtJ9Jwe914jzfC\n1xVpgr8SsgJfecL5iLTbuwYo/u1hp325S4Fh/MqHTeEbOiI9o0jXx53e0O8RAJGc/COLtPPb\nb3zi3zfxuRN56zRYJNL9ZoVv6Ii0oUjrzto9+t19JFJpS9dLVu5QlhwjrUPu+W0mnY9IUmXu\ntrXehmdaJL1o7VOzcfyyR3f+Om74ho5IG4q05qnd6m1QPo3w8sxuS5FKsZYjBzcO39ARaTuR\nhpvVvSL1Lu7kLMI0/s/lE8/sFou0Zoe6EDl94/ANHZGaF6l3eTeez2tuIntuFTycuP/nf5cs\nz8q9KiLthz+KSPOv5+kGr/Tui1S4e/H6rr9Hm3h4eayF8eWOS+cO/AMTzj+ySMVv6dNb2sQ1\n79tzyaGPa4o7NL1sLNX0wdPEbyft/cKMdXefHUTKFalwkDG135m+/02BRpcN7r1IpJGIZWRr\nW8q6pUSkZJEGVe47P1ywRg945kWaEqV/swmRevG3nLX4dUvpE2mV7evxywHHFalnTdGXsWGv\n/Wt76o2OkUa36goPrPG3nCcVaV3I1fgVgMOLJCLMi9TTqRvNzO6nt6srxN9ynlOktSlX4tcA\nnkqke84PF0ZvMPkE8BZgQqT+fut9j3T91NhzF0RaCXgukQo/XS3N2INl+5IZdQYidbr7kcd4\nFf6n+2ueu9yx7SDSHfg1gCcQSb+l66pOrrFuwiUD5Fafl52HF5XVGfxn9txejz8RtfgTrEH7\nuXlOkThGqjifWhQP8yfuMNhLyNY/uFlvQ58RqbD7eUCkrpv/AdbiLelZT3/X8ejxXx1IEOl2\ni+IGLVdM3qN0997Ry8clfU/1totn9oEnfo2iVGISeWv4gewD+AVrHCDSTIuprXadSIUzC71z\nAnLF56OMLiplKSUS5OQx0rhQKf9kyeGEb+hWkZYscvsizbUobbxyzS2iiFA6lz3J7ykyeb0+\nSv+B+1d0egpvnHJ8K83Xz3pjCqu/6H5bHYOtHUTaWqTxlvza3zhvbaLzCszcaHamk+qFA7fG\nvfVWkn/43aKbXKglvwUyubqzt1r0e4kLrlnDrzqI9Dp+7lXY7ErEWQUW3u72PaYfuHDYpffo\n/W/6IXt7rNEj9tdIyQv3ZYNbdeVvTa9Th3jTzJnHnefXnaMeIxV6TWzIgw2uG3i2yUxt1RM3\nHV2mNy7cs7QPHt9/cg1LWSdXt4+Uhx/eZ+IQbyrLapO8L/yYzxsg0vC76evEd4hzcUvtbXwb\nyjN8sOLqLwsxOhmxeCfae/jh497Yu5VSjo/HXm/c58lFWgAIEOnPyMY1+FJc/3lTpP2nEL94\nsyUX3VGs9MAfF4yYg6SjBy7oXPjqFChT300Wi/R+yyOLdPo9y0WaXtvxF7H3Rbp+WbqRaOYZ\ndvA8/NRFrwXFZqLeEGniktJN1x0jfdz2wCKdPj4sEWl6dWe2l9tXG2d5h67r/VbTgpsuoBQe\nWNIMgk1mLeyfpq2Z/LKNv6I3t4USGJGWiVRecbkmbxaV+Cxe45npeCknE/WSTa/4YL/VlQ/+\nCvzZB140n/dHpGcS6XX0sXT9+39mSgxOnHz+FsjUIxYfpX/N54OVHnhq5YsXFShrvnrzD7xk\nPu+PSL/ny2Vu3/i6YtPX6JQuW371wrk+8nl02fn6ceo+73cctzh/3kiuH/1bapzlEYuPMnz8\nEnF49cIZUW7edHiD+x93TH7g/oZp+xhp8PS8658f/rymk+/yo4/XmbrjkKJPZErx9Mb96wtV\nhDX7E3q86RwAAAN/SURBVM2JBys+Si/+xPKtO76fpty8uvh0b83x0O0HZo+0UKS5s3alp+fj\n7X36LkPRRncsUObizbizbsav4VmW4oabS4iVZjpYpUGkpSI9PA//cPoh/MPT+pby3HxEkirR\neOJb+YgkVaLxxLfyWxZp5SsbHp7Wl9rLD4/f/PJsKVJ/Dr/UXn54/OaXB5EawRPfykckqRKN\nJ76Vj0hSJRpPfCsfkaRKNJ74Vj4iSZVoPPGtfESSKtF44lv5iCRVovHEt/IRSapE44lv5SOS\nVInGE9/KRySpEo0nvpWPSFIlGk98Kx+RpEo0nvhWPiJJlWg88a18RJIq0XjiW/mIJFWi8cS3\n8hFJqkTjiW/lI5JUicYT38pHJKkSjSe+lY9IUiUaT3wrH5GkSjSe+FY+IkmVaDzxrXxEkirR\neOJb+YgkVaLxxLfyEUmqROOJb+UjklSJxhPfykckqRKNJ76Vj0hSJRpPfCs/SKTzzB+/bH2I\nb52c/Ih0e4hvnZz8iHR7iG+dnPyIdHuIb52c/GF/hJ1h2hxEYpgKg0gMU2EQiWEqDCIxTIVB\nJIapMJuIdPo9w3/rZY0P8a2TmX8LkU4fHz7/rZc1PsS3Tmh+RBoN8a0Tmh+RRlOKf9bPbc/k\n6odM6NaDSKN5SpGaP8b4mNCtB5FGUxYpZkssrv7pnLL6k1tP4+uPSKN5yj3SOSV+efkv38ba\nzo9Io0Ek64QuPyKNhvjWCc2PSKMhvnVC82/6yoaT/jvmaJ343snMz2vtGKbCIBLDVBhEYpgK\ng0gMU2EQiWEqDCIxTIVBJIapMIjEMBUGkRimwiASw1QYRGKYCoNIDFNhECl7/v2r607ff/+j\n6/47vZzPv7513bdfvWuYPQaRoudH9zbfLyK9dN/O59Plv1971zB7DCJFz9fuf+fzf113Eeni\nzN+Xj9+7f/QaZo9hocPn54+/X/6I9PN80edyWfeXXsPsMSx09rz8eQZ3Eeny3657///nNcwe\nw0JHz7fu6z8/fpZE+ryG2WNY6Oh5E+XXp0hfu/E1zB7DQkdP1/17/vXyKdL3y8mG/3Uveg2z\nx7DQ0fO96x8j/Xo7/d39p9cwewwLnT3fuu7l30+Rzj/fLuhdw+wxLDTDVBhEYpgKg0gMU2EQ\niWEqDCIxTIVBJIapMIjEMBUGkRimwiASw1QYRGKYCoNIDFNhEIlhKsz/AS5iFKwxomBuAAAA\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们得到了一个基本的散点图，其中每个点代表一个县。但是，它缺少一些基本组成部分，例如绘图标题，有意义的轴标签等。此外，大多数点都集中在绘图的底部，这不太好。您将在接下来的步骤中看到如何纠正这些问题。   \n",
    "像geom_point()一样，有许多这样的geom层，我们将在本教程系列的后续部分中看到。现在，让我们使用geom_smooth（method='lm'）添加一个平滑层。由于该方法被设置为lm（线性模型的简称），所以它会画出最适合的拟合直线。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5wBfN/lt/eyn0j93/oYkSrQ5NgnG0qJpF+WY83tTWb9HEm/0r60587bX5ot0ueJ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\nMAUGkRimwPwfZB1twzmxBloAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "geom_point() + \n",
    "# set se=FALSE to turnoff confidence bands\n",
    "# 设置se=FALSE来关闭置信区间\n",
    "geom_smooth(method=\"lm\", se=TRUE)  \n",
    "plot(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最合适的线是蓝色。您能找到其他method可用的选项geom_smooth吗？（注意：请参阅geom_smooth）。您可能已经注意到，大多数点都位于图表的底部，看起来并不好看。因此，让我们更改Y轴限制以关注下半部分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 如何调整XY轴范围(How to Adjust the X and Y Axis Limits)\n",
    "X轴和Y轴范围可以通过两种方式控制。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 方法1：通过删除范围之外的点  \n",
    "与原始数据相比，这将更改最佳拟合线或平滑线。这可以通过xlim（）和ylim（）完成。可以传递长度为2的数值向量（具有最大值和最小值）或仅传递最大值和最小值本身。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 5 rows containing non-finite values (stat_smooth).\"\n",
      "Warning message:\n",
      "\"Removed 5 rows containing missing values (geom_point).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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1n9l/Qvuoab47L9PbggeR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\nVBKqAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "# set se=FALSE to turnoff confidence bands\n",
    "# 设置se=FALSE来关闭置信区间\n",
    "g <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "geom_point() + \n",
    "geom_smooth(method=\"lm\")\n",
    "\n",
    "# Delete the points outside the limits\n",
    "# deletes points 删除点\n",
    "g + xlim(c(0, 0.1)) + ylim(c(0, 1000000))\n",
    "# g + xlim(0, 0.1) + ylim(0, 1000000)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这种情况下，图表不是从头开始构建的，而是建立在g之上的。这是因为先前的图g以ggplot对象存储为，该对象在被调用时将重现原始图。使用ggplot，您可以在该图的顶部添加更多的图层，主题和其他设置。  \n",
    "您是否注意到最佳拟合线与原始图相比变得更加水平？这是因为，当使用xlim()和时ylim()，指定范围之外的点将被删除，并且在绘制最佳拟合线（使用geom_smooth(method='lm')）时将不考虑这些点。当您希望知道移除某些极值（或离群值）时最佳拟合线将如何变化时，此功能可能会派上用场。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 方法2：放大\n",
    "另一种方法是通过放大感兴趣的区域而不删除点来更改X和Y轴限制。这是使用coord_cartesian()完成的。让我们将该图存储为g1，由于考虑了所有要点，因此最佳拟合线没有改变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "g <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "geom_point() + \n",
    "# set se=FALSE to turnoff confidence bands\n",
    "geom_smooth(method=\"lm\")  \n",
    "\n",
    "# Zoom in without deleting the points outside the limits. \n",
    "# As a result, the line of best fit is the same as the original plot.\n",
    "# 放大而不删除超出限制的点。因此，最佳拟合线与原始图相同。\n",
    "g1 <- g + coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000))  \n",
    "plot(g1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 如何更改标题和轴标签(How to Change the Title and Axis Labels)  \n",
    "我将其存储为g1。让我们为X和Y轴添加绘图标题和标签。这可以一次性使用来完成labs()与功能title，x和y参数。另一种选择是使用ggtitle()，xlab()和ylab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ChM2hQKlot08r5HpEdg0qZQsEik7hKo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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "# 画图\n",
    "# set se=FALSE to turnoff confidence bands\n",
    "g <- ggplot(midwest, aes(x=area, y=poptotal)) + geom_point() + geom_smooth(method=\"lm\")  \n",
    "# 限制范围\n",
    "g1 <- g + coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000))  # zooms in\n",
    "# Add Title and Labels\n",
    "# 添加标签，标题名，小标题名，说明文字\n",
    "g1 + labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pi0KRQgkj7SCpM2hQJE0kdaYdKmUJAv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\nUiCIREiB/D/pW/MzCnPUqAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 另外一种方法\n",
    "g1 + ggtitle(\"Area Vs Population\", subtitle=\"From midwest dataset\") + xlab(\"Area\") + ylab(\"Population\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "优秀！因此，这是完整功能调用。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ChM2hQKlot08r5HpEdg0qZQsEik7hKo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\nyBYine7/n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d5hyRmLWrS6TwNT/hZfkt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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full Plot call\n",
    "library(ggplot2)\n",
    "ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "geom_point() + \n",
    "geom_smooth(method=\"lm\") + \n",
    "coord_cartesian(xlim=c(0,0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 如何更改点的颜色和大小(How to Change the Color and Size of Points)\n",
    "本节主要内容有：\n",
    "+ 如何将颜色和尺寸更改为静态？(How to Change the Color and Size To Static?)\n",
    "+ 如何更改颜色以在另一列中反映类别？(How to Change the Color To Reflect Categories in Another Column?)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 如何将颜色和尺寸更改为静态？(How to Change the Color and Size To Static?)\n",
    "我们可以通过修改相应的几何图形来改变几何图形图层的美感。让我们将点和线的颜色更改为静态值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R9phUmbQgEi6SOtMGlTKEAkfaQVJm0K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\nxr3EjexwKyuLJCyXi1weRArb8ev6RpBYGvIhNMAFC5aKNH2Z5V7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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "# 画图\n",
    "ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set static color and size for points\n",
    "# 设置固定颜色和尺寸\n",
    "geom_point(col=\"steelblue\", size=3) +   \n",
    "# change the color of line\n",
    "# 更改拟合直线颜色\n",
    "geom_smooth(method=\"lm\", col=\"firebrick\") +  \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 如何更改颜色以在另一列中反映类别？(How to Change the Color To Reflect Categories in Another Column?)\n",
    "假设我们要根据源数据集中的另一列更改颜色midwest，则必须在aes()函数内指定颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x+k+dxLB0jT1UvGE7qtdiZIvBc+aaXSzcPEhPUGyq2urkPcPuIgkbULQFIzAkja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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "gg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set color to vary based on state categories.\n",
    "# 根据状态类别将颜色设置为不同。\n",
    "geom_point(aes(col=state), size=3) +  \n",
    "geom_smooth(method=\"lm\", col=\"firebrick\", size=2) + \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")\n",
    "plot(gg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，每个点都基于aes所属的状态（col=state）上色。不只是颜色，大小、形状、笔划（边界的厚度）和填充（填充颜色）都可以用来区分分组。作为附加的优点，图例将自动添加。如果需要，可以通过在theme（）函数中将legend.position设置为None来删除它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hApPVILkzaFAkRKj9TCpE2hAJHSI7Uw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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove legend 移除图例\n",
    "gg + theme(legend.position=\"None\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另外，您可以用调色板完全更改颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mOSwaewgoTDTQWQJIxQkJYPQOLsZqUx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\nAAmCGASQIIhBAAmCGASQIIhBAAmCGPT/LccH7xVRi78AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change color palette 更改调色板\n",
    "gg + scale_colour_brewer(palette = \"Set1\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在RColorBrewer软件包中可以找到更多这样的调色板，具体颜色显示见[网页](https://matplotlib.org/gallery/color/named_colors.html#sphx-glr-gallery-color-named-colors-py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>maxcolors</th><th scope=col>category</th><th scope=col>colorblind</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>BrBG</th><td>11</td><td>div </td><td> TRUE</td></tr>\n",
       "\t<tr><th scope=row>PiYG</th><td>11</td><td>div </td><td> TRUE</td></tr>\n",
       "\t<tr><th scope=row>PRGn</th><td>11</td><td>div </td><td> TRUE</td></tr>\n",
       "\t<tr><th scope=row>PuOr</th><td>11</td><td>div </td><td> TRUE</td></tr>\n",
       "\t<tr><th scope=row>RdBu</th><td>11</td><td>div </td><td> TRUE</td></tr>\n",
       "\t<tr><th scope=row>RdGy</th><td>11</td><td>div </td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>RdYlBu</th><td>11</td><td>div </td><td> TRUE</td></tr>\n",
       "\t<tr><th scope=row>RdYlGn</th><td>11</td><td>div </td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>Spectral</th><td>11</td><td>div </td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>Accent</th><td> 8</td><td>qual</td><td>FALSE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & maxcolors & category & colorblind\\\\\n",
       "  & <dbl> & <fct> & <lgl>\\\\\n",
       "\\hline\n",
       "\tBrBG & 11 & div  &  TRUE\\\\\n",
       "\tPiYG & 11 & div  &  TRUE\\\\\n",
       "\tPRGn & 11 & div  &  TRUE\\\\\n",
       "\tPuOr & 11 & div  &  TRUE\\\\\n",
       "\tRdBu & 11 & div  &  TRUE\\\\\n",
       "\tRdGy & 11 & div  & FALSE\\\\\n",
       "\tRdYlBu & 11 & div  &  TRUE\\\\\n",
       "\tRdYlGn & 11 & div  & FALSE\\\\\n",
       "\tSpectral & 11 & div  & FALSE\\\\\n",
       "\tAccent &  8 & qual & FALSE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 3\n",
       "\n",
       "| <!--/--> | maxcolors &lt;dbl&gt; | category &lt;fct&gt; | colorblind &lt;lgl&gt; |\n",
       "|---|---|---|---|\n",
       "| BrBG | 11 | div  |  TRUE |\n",
       "| PiYG | 11 | div  |  TRUE |\n",
       "| PRGn | 11 | div  |  TRUE |\n",
       "| PuOr | 11 | div  |  TRUE |\n",
       "| RdBu | 11 | div  |  TRUE |\n",
       "| RdGy | 11 | div  | FALSE |\n",
       "| RdYlBu | 11 | div  |  TRUE |\n",
       "| RdYlGn | 11 | div  | FALSE |\n",
       "| Spectral | 11 | div  | FALSE |\n",
       "| Accent |  8 | qual | FALSE |\n",
       "\n"
      ],
      "text/plain": [
       "         maxcolors category colorblind\n",
       "BrBG     11        div       TRUE     \n",
       "PiYG     11        div       TRUE     \n",
       "PRGn     11        div       TRUE     \n",
       "PuOr     11        div       TRUE     \n",
       "RdBu     11        div       TRUE     \n",
       "RdGy     11        div      FALSE     \n",
       "RdYlBu   11        div       TRUE     \n",
       "RdYlGn   11        div      FALSE     \n",
       "Spectral 11        div      FALSE     \n",
       "Accent    8        qual     FALSE     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(RColorBrewer)\n",
    "head(brewer.pal.info, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 如何更改X轴文本和刻度的位置(How to Change the X Axis Texts and Ticks Location)\n",
    "本节主要内容有：\n",
    "+ 如何更改X和Y轴文本及其位置？(How to Change the X and Y Axis Text and its Location?)\n",
    "+ 如何通过设置原始值的格式为轴标签编写自定义文本？(How to Write Customized Texts for Axis Labels, by Formatting the Original Values?)\n",
    "+ 如何使用预置主题一次性定制整个主题？(How to Customize the Entire Theme in One Shot using Pre-Built Themes?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 如何更改X和Y轴文本及其位置？(How to Change the X and Y Axis Text and its Location?)\n",
    "好了，现在让我们看看如何更改X和Y轴文本及其位置。这涉及两个方面：breaks和labels。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**第1步：设置breaks**  \n",
    "坐标轴间隔breaks的范围应该与X轴变量相同。注意，我使用的是scale_x_continuous，因为X轴变量是连续变量。如果它是一个日期变量，那么可以使用scale_x_date。与scale_x_continuous()类似，scale_y_continuous()也可用于Y轴。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAJWDCUCyIBC4ZyQSRgwVAuiAQsGMoF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\nIgSRCImQ/wNUC8BlXQ/SrwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "# Base plot\n",
    "gg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set color to vary based on state categories\n",
    "# 设置颜色\n",
    "geom_point(aes(col=state), size=3) + \n",
    "geom_smooth(method=\"lm\", col=\"firebrick\", size=2) + \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")\n",
    "\n",
    "# Change breaks\n",
    "# 改变间距\n",
    "gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**第2步：更改labels**  \n",
    "可以选择更改labels轴刻度。labels取与长度相同的向量breaks。通过设置labels从a到k的字母进行演示（尽管在这种情况下它没有任何意义）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MoFkYAFQ7kgErBgKBdEAhYM5YJIwIKh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\nBXkGAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "# Base plot\n",
    "gg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set color to vary based on state categories\n",
    "# 设置颜色\n",
    "geom_point(aes(col=state), size=3) + \n",
    "geom_smooth(method=\"lm\", col=\"firebrick\", size=2) + \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")\n",
    "\n",
    "# Change breaks + label\n",
    "# letters字母表\n",
    "gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = letters[1:11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果需要反转刻度，请使用scale_x_reverse()/scale_y_reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CofgMTZzUpjksGnsIKEw00FkCSMUFAq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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "# Base plot\n",
    "gg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set color to vary based on state categories\n",
    "# 设置颜色\n",
    "geom_point(aes(col=state), size=3) + \n",
    "geom_smooth(method=\"lm\", col=\"firebrick\", size=2) + \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")\n",
    "\n",
    "# Reverse X Axis Scale\n",
    "# 反转x轴\n",
    "gg + scale_x_reverse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 如何通过设置原始值的格式为轴标签编写自定义文本？(How to Write Customized Texts for Axis Labels, by Formatting the Original Values?)\n",
    "让我们设置Y轴文本的breaks，并设置X轴和Y轴标签。我用了两种方法格式化标签。方法1：使用sprintf()。（在下面的示例中，将其格式化为％）* 方法2：使用自定义的用户定义函数。（按1000到1K的比例格式化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bGs5OZYY0yFdTYggJSNY40xFdbZgABSNo41xlRYZwMCSNk41hhTYZ0NCCBl41hj\nTIV1NiCAlI1jjTEV1tl0Akil1ZY+0/JGpOaJd4ezjM+D0dRKLCu5OdYYU2GdDWhHkEZwDs6n\nyVGG6dZnmp9jjTEpBelw8oJkPZxVLCu5OdYYU2GdDWg3kDpoKJDshxyLZSU3xxpjKqyzASkE\naeDof7VWBAFBybUXSIeTD6QDRiR20/wca4xJ44g0nApNQJoc24llJTfHGmMqrLMB7QVSK0w2\npDHNz7HGmJaC9HlWunOkk3+yAdPf3Kb5OdYY0zKQPjupAAkXZHlN83OsMaZFIH1+Tkly8Rg+\n/Lhw3dlgkCSWldwca4ypsM7ygFQ5n5tBWiaxrOTmWGNMhXU2oAlHBkkOH1X/AZB2cawxpsI6\nGxBAysaxxpgK62xAy0Ey/gNIezjWGFNhnQ0IIGXjWGNMhXU2oEWTDQM944dHAKm02tJnqhwk\nc8YOIO3hWGNMhXU2oIUXZAHS/o41xlRYZwMyuAneIlSZnwCJ2XH9/gJtMQk4ritMW0xRrQH9\nX0AUIQCJ33H/Rh1NMQk47o95NMUU2RrQUpAWCiDFOx7f8aYnJgHH41m4nphiWwMCSLs7Bki7\nxRTbGhBA2tux+R5sLTEJODavVGqJKbo1IIC0t2OAtFtM0a0BAaS9HScA6XiWiGOANAgg7e1Y\nHKRjJ3bHs60ACSAldCw82XA8zpGEyYalrekEkACSpxEgxQggrXAsekH2eJwlKVFnL/uCLLMA\n0irHgrcI6QHpsm8RYhZAknOcP0hpTAESLbGs5OYYIO3uOJ0Akrra0jLZkMwUINESy0pujgHS\n7o7TCSDpqy0dF2TTmQIkWmJZyc3xelMNtwilMwVItMSykptjjTEV1tl0Akil1ZY+U4BESywr\nuTnWGFNhnU0ngFRabekzBUi0xLKSm2ONMRXW2XQCSKXVlj5TgERLLCu5OdYYU2GdTSeAVFpt\n6TMFSLTEspKbY40xFdbZdAJIpdWWPlOAREssK7k51hhTSZ19ff3LX90+AaSiaqugzr6+AqQV\nrRoda4ypkM6+tgJI0a0aHWuMqYTOvg4CSNGt7I77XzJoikmzYyUxvVoCSNGtzI7H39bpiUm3\nYwUxvU4EkKJbeR0bv/ZWE5Nyx7vHNMUIIK1oBUg7O943JooigLSmldWx+UQsLTFpd7xjTB6K\nANKaVoC0s+PdYvJjBJBWtAKknR3vE9Pr3xBHACm+FSDt7HiHmBpUABJva+LJhv7p8pfQ2d1N\n1zgeUAFIKdK9tnUGpPF9J5fQ2d1NYx1bqAAk6XRvag1ekDXewDUb0+NZTDGxmOYPkoMKQJJN\n9+bWwC1Cy0F67MQUE4Np5iBNUQFIgumWdWy+pThs+vgYICmPzqYwXeqYRAUgSdvQkygAACAA\nSURBVKVb3DFAYjZd5tiDCkCSSXcCx4tBenwMkZRHZ1OYLnHsRQUgSaQ7iWOAxGw66ziECkBi\nTzdXa/+iZa/p0skGgMTQOofKBYOUt646BVYxBqSgTJBYYyxFfzcrXawYkezWq0EB06UXZDHZ\nsKV12ZhzwSNS2nQzO14E0tJbhADS6tbFqAAk3lYmx1dXLkmbYsIF2VWtMagAJN5WnSB9f1+f\ntTEmVtMMQIpDBSDxtiYGqedjJqbrTptiYjXVDlI0KgCJtzUpSCMf4ZiurwMk7d9ZLaZD6xpU\nABJva8rJBoMPgMRg2rWuQwUg8bZqBOn6OkSSgs4qMa1bV6MCkHhb2Rw7HBGmJh8AicH0O0gD\nQOJOdyrHc7cIASRO01kaABJnumdbEzoGSFymi2gASMx7KnFt9ePSBpAw2RBsXUgDQGLeU0lr\nazxTwqydiOlyGgAS855KWVvG3N0WkHBBlm6NogEgMe8pPSAtviD7jVuEiNZIGgAS857iLwHv\naZB5fwNt2vORT2elHS80jacBIDHvKe4SCJwGzYO0YLP987xWmM61KspilOkqGgAS855iLoHQ\n0RsDSOMTJqNN51v1ZDHCdC0NAIl5T2UFkvHMY7aIWUz3AWkLDQCJeU/xlkCYlZnJhvnNAiRD\n22gASMx7KieQzPfCcEXMY5ocpM00ACTmPZUSpPAF2fnNAqRWHDQAJOY9lRSkfm585dQbQKrF\nQwNAYt5TCScbeq2eegNIcW9fAUiUhPZUepA2zBgUPtnASQNAYt5T3CUwfxokCdKmy7WKsjht\n5KYBIDHvKf4SaDGatPYPott0fBY23Hi5VlUWzUYJGgAS855KVFvjMx03nujMDEdbDvw0ZvG0\n5aURG1AJt/JXt08AyW41njK8ecZgdri6JJA21buQKUCKb80JJDlCt7auNd1a70KmACm+lcmx\n9UojqYHj0kDaXu9CpgApvhUgcbSuMGWpdyFTgBTfKgGS1OTa5YDEVe9CpgApvlUEJKnLPRcx\n2cBZ70KmACm+VWCyQS6m7EHirnchU4AU35oVSJlfkOWvdyFTgBTfyubYfcmeUEwyx4xbWxeY\nitS7kClAim9ldGy/q1JHTEocS9W7kClAim/d3XF/456mmJgdy9W7mGP+6vYJILE4Hm8l1xMT\ns2PJehdzzF/dPgEkDsfGj5vUxMTqWLjexRzzV7dPACnSsf0M4hJASlDvYo75q9sngBTl2H0q\nfttqPgAifUySjhPVu5hj/ur2CSDFOJ68p+WyQUpW72KO+avbJ4AEkOjGlPUu5pi/un0CSBGO\np++yvFSQEte7mGP+6vYpX5Dsqt0VpEubbEhe72KO+avbp1xBcgsXIHE53qPexRzzV7dPmYI0\nqdx9QeK4IEvOqy8zZWs97VTvYo75q9sngMQw2dBHtGGr9Lz6IlOu1h3rXcwxf3X7lCdI07P7\n3UHatlUxxwtbd653Mcf81e0TQIoqWqmBY1+Qdq93Mcf81e0TQIosWpFTGf/J10bHC1o11LuY\nY/7q9gkgyRVtBiApqXcxx/zV7VOeIO012SBlugtIiupdzDF/dfsEkIoESVm9iznmr26fMgVp\nnwuycqZpJxskihYgsUuyBAztcIuQnGlCkISKFiAt1eEs4vPQtY4ripVAbo5jTBNdkJUrWoC0\nUIfuH/LT5Ci7el/q+PMswZgS3CIkWbQAaaGCIJkc7V3vQo4/O2mKKa5VtmgBUow8IFkc5VRb\nyx1/fpIkZdNZ8aIFSDGiQRo4+l8t1vDUaARp70ji9bdcpUtyFEjD6GOBdChgRPo0pODXDstN\nk/3fX9mI9G8rLkzmtR2kybGdUPUoAmnXXzvEmKYp2q2mnI7/tcWFybxiQDqY/5Q12eCCtNOv\nHaJMxYuWy5TD8b+0mChZoAiQjInu4qa/QyB5XyyxZ2cli5bbdItjD0CaQTKvvE5AuvgLsg5H\n4y1x9quOLKj26qxU0WoDaQYipSAdDv2tDOSdDQZJQtWjEyTr5XvO+8P26KxU0UqarnG8ACKl\nIC2XUPUouSB77QfJfaPl1pjijxllilbaNMbxQoAA0trWBI5NjHqQrBeU84IUeD0mbcpetKlM\nlziOBOjff1tb/ur2qQSQqJvk1jme3KR99Gje71xr6IXNhCln0aY2DbauAmhwzF/dPl0+SPRN\ncusc6wSJr6QVgbQJoMExf3X7dPEgeW6SW+nY/bWDGEgTZx7Tpl7+nMVS0gpAYgFocMxf3T4B\npEjHzq8dyFOkZCA11fKnE0NJ7wjSSoAyvWl1kdZXjwBI5pVUVsddq1nubukLg9QVy58/Dkn5\ngbRhFAJIzK17gWROrqUEaSyWzEHaANGCzfJXt08AafO8+ljrTuVLTTZY1fPnj0tSHiBtBGjh\nZvmr26d8QXo8a940AUimrMKXAGlSPbmBFAvQvxf5w75F2lA9y1sfO82ack82pDH1XJAlqicX\nkKIBYogJIM22Pj46JO01axdjGtvqwSgzkNYCxBATQJptXQ4S6wXZHV9j5K0erZMNWwFiiAkg\nzbU+ProkMd4iFBhz0j4QdVCwerSBtAIguQtU/NXtUwkgLW6t3QXHnB1Amq8eRRdkV45CAImU\neG0JgfRockSicn3tNkt3dln1qLhFaCVEkjEBpLlWCZAe1YEkU1vcphsAEotpaOWvbp/yBClm\nsmFpq8MRhUpKkMRqi8uUASD2mCat/NXtE0BqWutXW4iDtHjmXLS2tprGAvRvgpi8rfzV7VOm\nIC2/ILuktX/Z0hxImyYbFs6ci9fW2sZVAAnHBJAYWpfdIhRq7d+wNL7+TxCkJaZJaiu6cQtA\nUjEtbeWvbp/yBWmr6fjOPwOk8GRD1AVZ+0BuFqRktbW4kQMg7phiW/mr26diQboiNQfS4hMd\nl7jgUJe0thY1xkOkYgJk0spf3T4BJBukgaT2Njf7/ZrLNzvh0QtS8tqabVw3EgEkdq0pvM2t\nsaY0R8PMXXu/qPvGZ1fmvUf2fUhLQdqhtrwt6wCSjWmrY/7q9gkgOSAZvwMaF5NOzbthnTtj\nXWyoR+Kd/BSJ1tZkyTaAZGLicsxf3T4BJFP2L7zDIJm/z3B/q2GD1LdZHO1WW8Nf0QApmACJ\nbeWvbp8AkgXSN8kRSdJikMbGHqRda0voJ3YAiV2c9b64lWWy4TsCpE+PmsYASDe71dZqgARj\nknbMX90+ASQHlvEX3jRI5mmPF6RvkqPPz5sbYycnq62tAEnElMgxf3X7VCxILknjbRL9L7wp\nkKYnPotB+u+stLUVPQQFIOKKKbFj/ur2qVyQXJSsO46c9n6JZ6CZguQQ91+ndLW1DqLdJ0C4\nHfNXt0+XBJJRxwtNHZAefa39Emoujpps6K3rz/8MpamtWID2/nEtQKK1GoZtIE1Led40AJLV\n1C3zzCFQIF1d/dPo9fVm5OjG2snstRU9Ail53ANAorUahk0gUYPCrKkL0qOvoV1KXx6qW5xt\nv/4zKAFI0QD1Akh8Khukb4ejR89iEiTPLUJNfRogjSTd2Dt5c22tAshwrOWReACJ1moYtrQS\n5/tLTLeBRP4W4o/FUT0k3TQo3dxwgRQLkO8ndgCJUWWCNLJBAjM54pueJE0dd/vuzwSkmqRG\nWwtkE0CEY4DEqBJBMn6mTgPjPXWiQHL2HQFSi9KGAuEAiHAMkBhVIEjmg1MiQXJ+r3e0nifX\n6A8J0oYCiYdoeeVhsoFPlwJSxGSDAZJn+tuzuFGLUX3TnPuE00Z/XJK2FMi6kehCQOqvuwEk\nZlS4QKrJ6AnxEWMtdXy9jic9ZhV20LCAtA6ghbVlftHxtGOqdbyEDZCYUeG6IDviY4N0bSAz\nLjVMh91zc2OT9Po6XDcaSnOWI6oEtgG0sLbsrxqedky0GjeDACRmVLhuETLosUEyH2pSL2g5\nIPaUC9If8/rrHxOlhSUQC5DG8xGAxK71qPRZWWG7nEGLHoujjqQ2hp6D6Z66uZkMSRRIf+ZL\nYO0IdPEgmfcnAqT4eo/M2TqQBnT+sURc6Rm2ag8uLki/flmTdON5h7c7sQBFvso7plXnoR1A\nqpUMpIiEGn//Q4q8Ztqauqc7Nki/fjkgjcU5iWkrQPGdnWnVOtkAkGplCNIABmE6rNTPx1og\n/SJAomNigiius+FWtdPfAKlWdiAZYNirNaYGRh1K7oA0D1I8REmKVi1ImGyopRykKUkESCMW\nDkf//WcatByZJNlbXT8KpShaxbcIAaRT1iBNVyFB6k3++EBaDxB3Z0OtikHCBdmTfpAmJDkg\nDbQYIJkH7b1Nve4vm6TtALF3NtCqGSTcIpQBSA5Kv/6aHFm4DJqA1DnqOFoD0P5FqxskFsf8\n1e1TmSCdCbAYebWGI4ukvzRILUlbRiAFRat3soHLMX91+1QqSD1JxmRco2UgxQL0L3GzkIKi\nBUh8KhwkY3ao0XTk6S7IDkujGfp3GMDs+XUNRav1giybY/7q9kkApPX6m1DuuU+7dAJSv+5K\nioYDyL/Didi4MUr1Ggl6P6jGKOX2Eitd7V7qiFRPAVCtw/LJEVuzdDqp0JrGAmT9lKLxY2DU\nbo3qzjjrMbbad8PNZ2Kn//trjCnzEUkBSP2stNs6Lp9OxjUruCDFAmTNhP01OHq1OaIvNBrz\n8H2re/A1nwmAZLTyV7dPFwnSr18OSX8nyz0g9SRFA/Q6mpMg3awDaTIdMJ8JgGS08le3TwDJ\nAikWIDum3lVX+uaN4y5H1M2Y5t2wAInFMX91+3SJIP365ZL0d7LcudAaC1BrPdR7qxGSHqTx\n3td1IE0vmc5nAiAZrfzV7dMlgdSDEwKpLmxjSIoFqDmGMycR/hnvZXFAemUYkQASQEoO0oiO\nH6S+sBuQVkFUywJpPDI0KWl/2DeZbABIiR3zV7dPuYPUHzr9NdjxgjQW9mqIXj0cnUmanHBt\nn7UDSAApCUhjnZogeScb1gBUm9q3JCwGyT4CZJi16z1rLFqNMQGkhTkz6tQahQiQVo9Af4fy\n7suY5sjWq7ti/T18QbZeOrkga4LkN53L0+JWgLRalwFSXV/UkMTwE7u/BkZtGftAMn/WaYf3\nj/nLWXpY6S37wa9vnXK09Uej+kwBEq1kILnVbA5DmwEatnqubPsQ7tUD0h/jTys+GySyOy6D\nRqt5XAeQIlv5q9unCwBprLBf8T+xqx0EN9tgtAikcdS6aUH6G+JoMUiTNTY+WEefKUCitQtI\nsQAZM3i/wiBND+EmQ2HH0c3war52amI5SLZ7srMAaU0rf3X7lD9Iq25LeCVBokqeAqld8z9b\n5lUgCqRQKqZvdxpbj2c5q0zfExhRW6sbAVJIGYDUn/aQkw0rIGrq2rw4O0yOT0eP7sCOuLv1\nNcRRDdK5s8ZLLcKp8IN07OQDaS7HcZUHkFZLPUjjDILVGgtQP5vQkzIFiTgOu7lZCtJ5RfNN\nze3T+M1n9K8E6XgkSGJ4pwd/0QIkdq3fyURWjEntpnUdQH8db6/uod0/pvpt34wgTX6TPnJ0\nruwRIOv9MMtB+nY5kgbJ17i+aAESu9bv5BBIK0cgK93mpSaDm7//OOqsRoycB0TWjf8Zg4iJ\nUYNS253tIB2PBEnCb5mytLxoARK71u/kaVY2AuSk2wDJZWcC0o2JkUHSP93blD6nILnYMIM0\nvkktHUieVu56FzIFSK/xx3DjQZc33eZtRDMg3bgcnVWX8fheMhOkIw2SMdnQguBPxTKQlmUx\nAUi+RgkaAFL8nqqzsmoE+i+IUTxINy5ILQ7Gy/7MIel4dDjqSLJBclFaMtmQG0iuOGgASPF7\nahVEy9O9FCRnQLoiZYDUTjBMQToeR+yur12SYmftFmdREUie1igaAFJ8uldCtDTdi0FqB6QJ\nO867mw2Omu8ESC1K/VzETiAZB4xztvswGL4QD5DiE7oCoKh0L5tsqAckd4ShQGpRGo/eSJCO\n3XHfSBI1Y0CD5Dmwiyta59xLJUi+VoC0MqFhgrb/f8sAybyO5GD0/e3OHXQcTUC6vrZuXnA4\nIkGi57CN48TrPlFejKLK0jOL4YhhOjDB4SZAWprQvyRA/luEYkFyX2c0HYzaOCiQvr8fH63J\nhukEgwvSyeHo+tp3VdUFqZ3CoCmazWI0SDTckZvd4bwNIPlkgRTHytjYc+dv9VBkXOyhZ+HM\nMYcC6dtZOwhSU7h9gY8nXPVdEf3fDCB5T77IdTIDydcKkP4GToIWgmT+uM9jSkNkXTN1KemL\n+ehwNIHN8tMenpkgfZJqt9diZMHmIwkgLW1NJ10ghVBZBJJ5E9BSkFoMHh9NDDwjkjMNR65j\nd3YJSJ89uQ1HqUEyVwFIq1UeSARELUk2EBQk7uTB9fXRXYXorO/IzgOS2TC5OWhR9Rh/i4Jk\nBXWiF0dHHNcIkEhtBKmZghtUP+mAmqegIJrIHW368crGiAskg6RvCiQbhdZxu3I4x2dblyM+\nkDx+3Vg9W13eCpDi+70JpGE2ewBpHJwmIIUg6lGyQTIRM0AaSAp0doTEPySNZJjLm/shjpNV\nT6ZL72aPR0mQXMcnerFvq8tbAVJ8v7eAZFwX6kAyDvMskOYhaomxQDIJux5+d3Td37ww09mh\n5gMg9bJBIs6rzo7NUyrfZrsTLHcTXJMNAMlWaSC1F4PqHR0zJDlXkMY//dd6rM62q0xGF7ro\nLI485PlBGhYO16HsDTCBNIn+RC/2bnVxK0CK7/cGkMwZBJej/sfkzTXVxxajJSiRA1I7VHXm\nA0gkStYxllXXIY4skDwcGSjZtuNS340R0z0wXsMCSOtVCkgtRA1I465ezJED0mB/7alWq7NH\nR9MDPNvMuDgbAOmTAsk45JsFqW/oDx/rz7qxDWgaFkAK6lJB+mOA9Ph4GjBaAdLjow2S6WAW\nJJei4+TZWm7BHo3CDnDkA6lrmgNpbDLnTk7ByABSQJcJUv2Y3x4iV+a+XkbRMpCo66YERt2K\nbaVNi9Uu7BBIn1OQzDuNwiAZbRZI7ja8O29ymy0mG9i1vt8LQSLvphs56p87T0AUD9KUJPtH\n5kGQaI6OYyqo6YI4kGxTs7o9gQEkGeUHkuduOgekc4nQHM2DZDeEQbq68tRr01kHoJ68PhX2\nfEFffEIg9Ss3e8DC2jixmm7Et/Ps22yNRpIjgLRG6/sd9zNXp3HkqC+RVSC5TY8OSdfXrgMv\nSFOMWpS6VBgFbNbf9VKSpsOZTYAZ17glNpCM22ztxilGAGmV1vd7E0jtedKIUV1BU4yur8OT\nDZPG6+vaqL+HwS3uK4OkSXdIjppBqUkFTcviIYk4KnQJsIajjpUpSJ7bk8IgWbfZzu9ZgLRC\n6/s9D5I5p/3LbXx9/WMVCIVRLEjXUzGA1JcvwcfiIYnIn7c5DBJ9exJAilJWIP36FQTp235Y\niHUw13M0ocDStHkJSFc9KE53aI56ksK/ThorNMiRvVUfAWb0k8kGa/eEOHJ2nh0lQOJ3ub7f\nMyD9CoBUO65ZmYLU1wTNgHdAalcgODK9XPUg0YUXBslDSD9atT6IwrYxsrbqISAAkoHCdxxI\nzpwjQGLX+n6vBKl13I8/Ho7sG25IiiYgkRg5I0kDEl15p+M4UzcB6egHqY5+Mps8bmDYzHSr\ni0Fyflo17h4/RxOvMXsWIK3Q+n6HQHIp6jiqJxcax+OJUHe8shkkD0aTQzIPSLYrFxXf7asj\ndhMnkxqfbpWK45sEqUFpXGrsntba9UH6HVpm9yzd2jsESKTW99sP0hSjBqR+csECqXM2ney2\nq38GIz9Hk6HEYqLburOK/YTjEEjX5nGiL/qmNbhVO7EGMe4ctgGSfZhnyed4BCy+3kePAInU\n+n57QaI5Mk+Zzbm52lV/FclXhnMYLR+QSJDcVWJAMnz5op801tu0I7MSS4NkdmoyeWDL49dY\nHl3vhkuARGp9vyNAOvY/8CFBGi/G+srwcTVH4dsNPCAZJFHTD8Z6ZsPMpqytupHZmR0p8YHk\njE62aMfWFgESu9b32weSA1E7odCeDvWlaYFkfPNV3gxI7rMZIkGaLu4NR/QnM2TtWt5zp/BW\nJ5G5ue0RYQNpboMz+900BUik1vd7EUgtQDYmNkjmF/P2M+PvxwlI5o4154YZQbJ+3DABqV3D\nHqmC27KOt2ZBIvaA7/8UUyPC8eINeva7aQqQSK3t9+Pj9KbudmLOwWjC0bn0F4BkKjwg2Zd/\n4khqujJd3JfoeJnLnWpuMVoJ0iSyRXuAHJAA0iqpAamuevcqa4vRCNIwvT0F6Uhz9EiX/XRA\nIkEyazwA0vVk+nuyQaNEj930/Mldb/Lcn/Bvzb/Nea9pYEv2wGKQppMNyzfo2e+mKUAitarf\njz1Iv6Yc/Rkh6q63Ts9//CBNi7FevgQks6BDJE1BImbtJhU6ueT6PSEpOP4N1vXnOpCMOYgw\nSNO3ZUwygckGdq3qdxAk67jNA9JxXMFc/dHd4d2AFpr9ns6rddVFo2TeNt13xyl6qkJPkxXr\n7xS/n5Tc38auAsm4dhQckCa/PgJIrpSA9DiC9MvlyJlJ8IHUz+NRILlrnteZB2lals7AZP0g\nxyleAiOnQmmQzGXkAOj8DKjXWpBsB36Q3M26seGC7GrLw1n1R/dtbFjTby9IR5eMAEiDFoBk\nvV/vynJHXehpo3RB8lfed1snobX8IDm2U7IIhy5y0XsgeEGW2Kz1f5OQ38BW+0CLBunQ/XMY\nv3Va028apGGSy+GImmygQTpO/5/erzNANA5mYwXSIHlI8nc2tEYIpG/3YapTjMgn2m0BKXiL\n0HSr7hLctLpSFkgmR0wgtcs9IE1upOsWuyQd/SB150nGDy+O1o9zZkD6Dv8vfOisfwVqssFs\n9VeuZ7NmR70xrSxaYrPOAoC0UiZIFkcMkw3G8gkZXb3bJA0cWINS/XntkuQMXNZI5sRE1Lhd\nPUGMFpaAF6SJ+zmQRn8LNhvbSG3V+g6QVsoAaeDof7XWuRtBsl9XSFS9YXE6mWyYq/fyg2Ru\n4WQuGDQaGQtrb+u66BGxDZ9MjlhjWKJdNpqPGEA6cIxI/QXZR3d5YMSwV6JWn5JEOfIGJXWg\n5LRSm1h3KhO1WTWmGJEakCbHdmv7XaNELe8rf6FjB5Thf+FtuVI8BhwLHShtbNXoWGNMeYE0\nfOkllpV1jbNzAoXVlj5TgMQ3/b25NdS46Vjo4mpLnylAMmYaNl6Q3dyq0bHGmArrbDpx3dlg\nkCSWldwca4ypsM6mk5J77Ta3anSsMabCOptOAKm02tJnCpBoiWUlN8caYyqss+kEkEqrLX2m\nAImWWFZyc6wxpsI6m04AqbTa0mcKkGiJZSU3xxpjKqyz6QSQSqstfaYAiZZYVnJzrDGmwjqb\nTgCptNrSZwqQaIllJTfHGmMqrLPpBJBKqy19pgCJllhWcnOsMabCOptOAKm02tJnCpBoiWUl\nN8caYyqss+kEkEqrLX2mAImWWFZyc6wxpsI6m04AqbTa0mcKkGiJZSU3xxpjKqyz6QSQSqst\nfaYAiZZYVnJzrDGmwjqbTgCptNrSZwqQaIllJTfHGmMqrLPpBJBKqy19pgApXitf+rLVNjvT\n/CLOr7O8AkgqTfOLOL/O8gogqTTNL+L8OssrgKTSNL+I8+ssrwCSStP8Is6vs7xKDBIEXaYA\nEgQxCCBBEIMAEgQxCCBBEIMAEgQxSBKk7i2z1t/jZ7s4xnZ4hXrYdsNmmU371dd09tC/ozdl\nxAf5rXJFPBbDss1KSxCkw/CP8w70w/CnP9uEbZ+sGdsNm2U27SNe1dnxe9qIhfPEFbFRDIs2\nK66cQDpYbnIA6dDbZATSgog1gHQwvhYPkr/bnr3spC3GdMlmuU0P5kdcZydbTmJqB74BhhWd\njcux9XXBZsUFkARNt4DUn6ysKenVppYXgBSjnUBqjnAPp9PB0/XQXp6x9dFwWGtqnM3GmR6W\nbXVLomgGV5uO/8abHhblyZ+ouIitrwsKSlx7gTR8XbGXw7ZBkNaZLoiYMHU9xHZ25WaX5pg5\nxZv2bM1gXMTW1wWbFVcxIG1hcCVIw2Ry2FQVSFadJgMpPmIq6EJBOpzi020mfWWBxJoujNhX\nH8ZHVGc3bHZTxNJ54oqYCDq4WXEVApKd/kxAWmALkBZuVlwp7mw4mH/3Bzvdv75uU7ZOdXpP\nZ6em40FWtOnSiOmAT6s7u2GzWyJ24ubfKlfE47rLNist3GsHQQwCSBDEIIAEQQwCSBDEIIAE\nQQwCSBDEIIAEQQwCSBDEIIAEQQwCSEpUVdgVOQt7T4feziC97R0EtF4ASYceqvvqYe8goPUC\nSDpUVT/tsV1VfRzuTqefh6p6+KkXvN9X1eFp3+igWQEkFXo7D0cPzbFdVd3VQ9PhfKhX3Z7a\nY76zQJJyASQVqiF6a47tWmae63+fqpfT6bb6fTp9YCZCu7CDVKgBpfvn61Tj0yy9r//9enu+\nA0jahR2kQd3xW31s158ptTr/eTf8BSkWdpAGPXTcPExBeqhuX96+AJJ2YQdp0KGqJ+h+qkMP\n0u2wX5rvPwBJu7CDFOi9u4T0UL13ID3Vkw2/q7sapPfTD86R1As7SIGezrDUejvj0yLz00x/\nVx91G86RchB2kAKNzxw6dCCdvs6nTXcNXs0fAEm7sIMgiEEACYIYBJAgiEEACYIYBJAgiEEA\nCYIYBJAgiEEACYIYBJAgiEEACYIYBJAgiEEACYIYBJAgiEEACdquavysnEUzhhy/EYnbpJAA\nErRdHQomEeHCcmp/WxWqqGEVQUCZaytI28pQRQ2rCALKXFVTR1V/aFcfrLWLhn/6A7j2czic\nM0HqFlanfpVx/d5l0+iuaXy620kogARtlw1S94y+ym4Yz6CIMymzaXzEn71+VY3LjTWtld3t\npBNAgrZrCtLwJ1HgpzBIw3Ji4ViudON+8w4ACdouGxkKpH5yjjik69fuVlkCkr0mAdIp+UMu\nABK0XT6QzHFqWHc4OrM/rUVhkIjjPhek5CgBJGi7YkDyHeItBynQ6G4nW9D4SQAAAJNJREFU\nnQAStF3V+F9Xzca4M/xFnOtUJ+8qk/Wt4z5z5sEFyXKSSgAJ2i4bpH6u2gDJnZYepr+NOxuq\nypqTG1z1dsah3TgX7iKI6W8ImpFbq6pqV1UwEESLPFRTVbuqgoEgj6hDNVW1qyoYCMpVAAmC\nGASQIIhBAAmCGASQIIhBAAmCGASQIIhBAAmCGASQIIhB/w/DDvS7pIpRDgAAAABJRU5ErkJg\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "# Base plot\n",
    "gg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set color to vary based on state categories\n",
    "# 设置颜色\n",
    "geom_point(aes(col=state), size=3) + \n",
    "geom_smooth(method=\"lm\", col=\"firebrick\", size=2) + \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")\n",
    "\n",
    "# Change Axis Texts\n",
    "gg + \n",
    "# 更改x轴\n",
    "scale_x_continuous(breaks=seq(0, 0.1, 0.01), labels = sprintf(\"%1.2f%%\", seq(0, 0.1, 0.01))) + \n",
    "# 更改y轴\n",
    "scale_y_continuous(breaks=seq(0, 1000000, 200000), labels = function(x){paste0(x/1000, 'K')})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 如何使用预置主题一次性定制整个主题？(How to Customize the Entire Theme in One Shot using Pre-Built Themes?)\n",
    "最后，我们可以使用预先构建的主题来更改整个主题本身，而不是单独更改主题组件。帮助页面?theme_bw显示了所有可用的内置主题。这通常是通过两种方式来实现的。在绘制ggplot之前，使用theme_set()设置主题。请注意，此设置将影响将来的所有绘图。或者绘制ggplot，然后添加整个主题设置（例如theme_bw()）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gw3Vvk45aawxJm2xQLu6k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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "\n",
    "# Base plot\n",
    "gg <- ggplot(midwest, aes(x=area, y=poptotal)) + \n",
    "# Set color to vary based on state categories.\n",
    "geom_point(aes(col=state), size=3) +  \n",
    "geom_smooth(method=\"lm\", col=\"firebrick\", size=2) + \n",
    "coord_cartesian(xlim=c(0, 0.1), ylim=c(0, 1000000)) + \n",
    "labs(title=\"Area Vs Population\", subtitle=\"From midwest dataset\", y=\"Population\", x=\"Area\", caption=\"Midwest Demographics\")\n",
    "\n",
    "gg <- gg + scale_x_continuous(breaks=seq(0, 0.1, 0.01))\n",
    "\n",
    "# method 1: Using theme_set()\n",
    "theme_set(theme_classic())  \n",
    "gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4rf+xa3gkjA/UJGfwOBAhCRgEABiEhA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\nOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# method 2: Adding theme Layer itself.\n",
    "# 添加主题层\n",
    "gg + theme_bw() + labs(subtitle=\"BW Theme\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gg + theme_classic() + labs(subtitle=\"Classic Theme\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
